Abstract
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions, which are crucial for reducing scan times. While deep learning techniques have advanced image reconstruction, the recent introduction of diffusion models offers new possibilities for imaging tasks, though their application in the medical field is still emerging. Notably, diffusion models have not yet been explored for the MRF problem. In this work, we propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction. Qualitative and quantitative comparisons on in-vivo brain scan data demonstrate that the proposed approach can outperform established deep learning and compressed sensing algorithms for MRF reconstruction. Extensive ablation studies also explore strategies to improve computational efficiency of our approach.
Abstract (translated)
磁共振指纹识别（Magnetic Resonance Fingerprinting, MRF）是一种时间效率高的定量MRI方法，能够通过单次加速扫描映射多种组织属性。然而，在高度加速和欠采样的采集情况下实现准确的重建仍然具有挑战性，而这些技术对于减少扫描时间至关重要。虽然深度学习技术已经提高了图像重建能力，但最近提出的扩散模型为成像任务提供了新的可能性，尽管它们在医学领域的应用仍在发展中。值得注意的是，目前还没有探索扩散模型用于解决MRF问题的方法。在这项工作中，我们首次提出了一种用于MRF图像重建的条件扩散概率模型。使用体内脑部扫描数据进行的定性和定量比较表明，所提出的这种方法可以超越现有的深度学习和压缩感知算法在MRF重建中的表现。广泛的消融研究也探讨了提高我们的方法计算效率的策略。
URL
https://arxiv.org/abs/2410.23318
